Outage-Capacity-Based Cross Layer Resource Management for Downlink NOMA-OFDMA Video Communications: Non-Deep Learning and Deep Learning Approaches

Abstract

Prior works either considered outage capacity of wireless video transmission systems but did not consider NOMA which is a key technology for 5G ultra-reliable low-latency (URLLC), or concerned the ergodic capacity of NOMA-OFDMA systems but did not consider the outage capacity emphasized in 5G URLLC scenario. In this paper, outage capacity (as well as ergodic capacity) maximization in a 5G URLLC scenario, are considered, using two proposed resource management schemes (i.e. B, C) and finally, proposed deep-learning-based versions of Schemes B and C (i.e. Schemes B' and C') to reduce the complexity and the latency for 5G URLLC. The proposed schemes re-allocate subcarrier according to outage capacity- instead of ergodic capacity only-maximization objective and choose the candidate user to gain subcarrier in a new way to improve the outage capacity. The numerical results show the proposed Scheme B and C increase the outage capacity (the percentage of satisfied users) from 79.2% for a prior work (Scheme A) to 85.2% and 92.6%, respectively. Scheme C also increases the ergodic capacity (average PSNR) from 34.7dB for Scheme A to 35.5dB. The deep learning based Schemes B' and C' perform slightly poorer than the corresponding non deep learning based Schemes B and C but the execution time/latency of Scheme B'/C' is less than Schemes B/C.

Keywords

• Outage capacity
• ergodic capacity
• ultra-reliable low-latency communications
• multimedia communications
• non-orthogonal multiple access
• channel-aware